摘要
The joining of a 6-mm thickness Al 6061 to Stainless steel 304 has been performed by solid state welding. A selection method of optimum friction welding condition using neural networks is proposed. The data used for analyses are the friction stir welding condition, the input parameters of the model consist of welding speed and tool rotation speed. The outputs of the ANN (Artificial Neural Network)model includes resulting parameters, namely, maximum reached temperature,and heating rate for both aluminum alloy 6061 and stainless steel 304 during friction stir welding process.The results of analysis suggest that the proposed method is an effective one to select an optimum welding condition.Good performance of the ANN model was achieved. The combined influence of welding speed and tool rotation speed on the maximum reached temperature and heating rate for both aluminum alloy 6061and stainless steel 304 friction stir welding was simulated. A comparison was made between the output of the ANN program and finite element model. The calculated results were in good agreement with that of finite element model.
The joining of a 6-mm thickness Al 6061 to Stainless steel 304 has been performed by solid state welding. A selection method of optimum friction welding condition using neural networks is proposed. The data used for analyses are the friction stir welding condition, the input parameters of the model consist of welding speed and tool rotation speed. The outputs of the ANN (Artificial Neural Network)model includes resulting parameters, namely, maximum reached temperature,and heating rate for both aluminum alloy 6061 and stainless steel 304 during friction stir welding process.The results of analysis suggest that the proposed method is an effective one to select an optimum welding condition.Good performance of the ANN model was achieved. The combined influence of welding speed and tool rotation speed on the maximum reached temperature and heating rate for both aluminum alloy 6061and stainless steel 304 friction stir welding was simulated. A comparison was made between the output of the ANN program and finite element model. The calculated results were in good agreement with that of finite element model.
